Overview

Dataset statistics

Number of variables52
Number of observations110393
Missing cells0
Missing cells (%)0.0%
Duplicate rows14623
Duplicate rows (%)13.2%
Total size in memory43.8 MiB
Average record size in memory416.0 B

Variable types

Numeric7
Categorical45

Alerts

Dataset has 14623 (13.2%) duplicate rowsDuplicates
horizontal_distance_to_hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with horizontal_distance_to_hydrologyHigh correlation
wilderness_area1 is highly correlated with wilderness_area3 and 1 other fieldsHigh correlation
wilderness_area3 is highly correlated with wilderness_area1High correlation
soil_type_29 is highly correlated with wilderness_area1High correlation
elevation is highly correlated with wilderness_area4High correlation
horizontal_distance_to_hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with horizontal_distance_to_hydrologyHigh correlation
wilderness_area1 is highly correlated with wilderness_area3 and 1 other fieldsHigh correlation
wilderness_area3 is highly correlated with wilderness_area1High correlation
wilderness_area4 is highly correlated with elevationHigh correlation
soil_type_29 is highly correlated with wilderness_area1High correlation
wilderness_area1 is highly correlated with wilderness_area3 and 1 other fieldsHigh correlation
wilderness_area3 is highly correlated with wilderness_area1High correlation
soil_type_29 is highly correlated with wilderness_area1High correlation
wilderness_area4 is highly correlated with classHigh correlation
wilderness_area3 is highly correlated with wilderness_area1High correlation
wilderness_area1 is highly correlated with wilderness_area3 and 1 other fieldsHigh correlation
soil_type_29 is highly correlated with wilderness_area1High correlation
class is highly correlated with wilderness_area4High correlation
elevation is highly correlated with Horizontal_Distance_To_Roadways and 4 other fieldsHigh correlation
horizontal_distance_to_hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with horizontal_distance_to_hydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with elevation and 2 other fieldsHigh correlation
wilderness_area1 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
wilderness_area3 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
wilderness_area4 is highly correlated with elevation and 3 other fieldsHigh correlation
soil_type_6 is highly correlated with wilderness_area4High correlation
soil_type_10 is highly correlated with elevation and 1 other fieldsHigh correlation
soil_type_29 is highly correlated with wilderness_area1 and 1 other fieldsHigh correlation
soil_type_40 is highly correlated with elevationHigh correlation
class is highly correlated with elevation and 1 other fieldsHigh correlation
horizontal_distance_to_hydrology has 4631 (4.2%) zeros Zeros
Vertical_Distance_To_Hydrology has 7308 (6.6%) zeros Zeros

Reproduction

Analysis started2022-05-09 08:54:11.496192
Analysis finished2022-05-09 08:56:36.998988
Duration2 minutes and 25.5 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

elevation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1769
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2957.980941
Minimum1871
Maximum3850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:37.266477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1871
5-th percentile2405
Q12808
median2995
Q33162
95-th percentile3335
Maximum3850
Range1979
Interquartile range (IQR)354

Descriptive statistics

Standard deviation280.1726811
Coefficient of variation (CV)0.09471754102
Kurtosis0.7377532562
Mean2957.980941
Median Absolute Deviation (MAD)175
Skewness-0.818958447
Sum326540390
Variance78496.73123
MonotonicityNot monotonic
2022-05-09T14:26:37.603731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2975332
 
0.3%
2965330
 
0.3%
2978328
 
0.3%
2968327
 
0.3%
2962323
 
0.3%
2998319
 
0.3%
2952314
 
0.3%
2995312
 
0.3%
2988311
 
0.3%
2959310
 
0.3%
Other values (1759)107187
97.1%
ValueCountFrequency (%)
18711
 
< 0.1%
18741
 
< 0.1%
18761
 
< 0.1%
18802
< 0.1%
18833
< 0.1%
18851
 
< 0.1%
18871
 
< 0.1%
18881
 
< 0.1%
18912
< 0.1%
18941
 
< 0.1%
ValueCountFrequency (%)
38501
 
< 0.1%
38492
< 0.1%
38463
< 0.1%
38451
 
< 0.1%
38441
 
< 0.1%
38422
< 0.1%
38391
 
< 0.1%
38371
 
< 0.1%
38321
 
< 0.1%
38281
 
< 0.1%

aspect
Real number (ℝ≥0)

Distinct361
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.3881768
Minimum0
Maximum360
Zeros951
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:37.920797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3259
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)201

Descriptive statistics

Standard deviation111.7553172
Coefficient of variation (CV)0.7192009039
Kurtosis-1.21189034
Mean155.3881768
Median Absolute Deviation (MAD)85
Skewness0.4085339239
Sum17153767
Variance12489.25092
MonotonicityNot monotonic
2022-05-09T14:26:38.218455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451263
 
1.1%
0951
 
0.9%
90833
 
0.8%
135744
 
0.7%
72677
 
0.6%
315659
 
0.6%
18639
 
0.6%
63634
 
0.6%
27627
 
0.6%
108600
 
0.5%
Other values (351)102766
93.1%
ValueCountFrequency (%)
0951
0.9%
1313
 
0.3%
2373
 
0.3%
3375
 
0.3%
4433
0.4%
5367
 
0.3%
6393
0.4%
7419
0.4%
8445
0.4%
9460
0.4%
ValueCountFrequency (%)
3605
 
< 0.1%
359258
0.2%
358336
0.3%
357393
0.4%
356403
0.4%
355363
0.3%
354403
0.4%
353357
0.3%
352370
0.3%
351421
0.4%

slope
Real number (ℝ≥0)

Distinct57
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.09516908
Minimum0
Maximum65
Zeros125
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:38.552083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum65
Range65
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.471290895
Coefficient of variation (CV)0.5300603954
Kurtosis0.5531289377
Mean14.09516908
Median Absolute Deviation (MAD)5
Skewness0.7850979096
Sum1556008
Variance55.82018764
MonotonicityNot monotonic
2022-05-09T14:26:39.040112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106447
 
5.8%
126371
 
5.8%
116316
 
5.7%
136176
 
5.6%
96048
 
5.5%
145814
 
5.3%
85680
 
5.1%
155590
 
5.1%
75035
 
4.6%
164975
 
4.5%
Other values (47)51941
47.1%
ValueCountFrequency (%)
0125
 
0.1%
1646
 
0.6%
21469
 
1.3%
32151
 
1.9%
43123
2.8%
54055
3.7%
64718
4.3%
75035
4.6%
85680
5.1%
96048
5.5%
ValueCountFrequency (%)
651
 
< 0.1%
602
 
< 0.1%
571
 
< 0.1%
562
 
< 0.1%
521
 
< 0.1%
512
 
< 0.1%
503
 
< 0.1%
497
< 0.1%
488
< 0.1%
471
 
< 0.1%

horizontal_distance_to_hydrology
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct487
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.7807923
Minimum0
Maximum1361
Zeros4631
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:39.381984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3390
95-th percentile684
Maximum1361
Range1361
Interquartile range (IQR)282

Descriptive statistics

Standard deviation212.2580009
Coefficient of variation (CV)0.7867795151
Kurtosis1.316420545
Mean269.7807923
Median Absolute Deviation (MAD)133
Skewness1.126251265
Sum29781911
Variance45053.45895
MonotonicityNot monotonic
2022-05-09T14:26:39.718163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306682
 
6.1%
04631
 
4.2%
1503770
 
3.4%
603576
 
3.2%
672812
 
2.5%
422712
 
2.5%
1082665
 
2.4%
852645
 
2.4%
902175
 
2.0%
1201983
 
1.8%
Other values (477)76742
69.5%
ValueCountFrequency (%)
04631
4.2%
306682
6.1%
422712
2.5%
603576
3.2%
672812
2.5%
852645
 
2.4%
902175
 
2.0%
951746
 
1.6%
1082665
 
2.4%
1201983
 
1.8%
ValueCountFrequency (%)
13611
< 0.1%
13561
< 0.1%
13432
< 0.1%
13281
< 0.1%
13211
< 0.1%
13191
< 0.1%
13171
< 0.1%
13101
< 0.1%
13021
< 0.1%
13012
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct562
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.55246257
Minimum-173
Maximum590
Zeros7308
Zeros (%)6.6%
Negative10353
Negative (%)9.4%
Memory size862.6 KiB
2022-05-09T14:26:40.053614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median30
Q369
95-th percentile165
Maximum590
Range763
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.07499462
Coefficient of variation (CV)1.247517133
Kurtosis4.837872641
Mean46.55246257
Median Absolute Deviation (MAD)27
Skewness1.737085245
Sum5139066
Variance3372.705
MonotonicityNot monotonic
2022-05-09T14:26:40.382689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07308
 
6.6%
31807
 
1.6%
61663
 
1.5%
101655
 
1.5%
71652
 
1.5%
41601
 
1.5%
131554
 
1.4%
51447
 
1.3%
161410
 
1.3%
91405
 
1.3%
Other values (552)88891
80.5%
ValueCountFrequency (%)
-1732
< 0.1%
-1572
< 0.1%
-1541
 
< 0.1%
-1521
 
< 0.1%
-1473
< 0.1%
-1452
< 0.1%
-1442
< 0.1%
-1432
< 0.1%
-1411
 
< 0.1%
-1391
 
< 0.1%
ValueCountFrequency (%)
5901
 
< 0.1%
5851
 
< 0.1%
5823
< 0.1%
5761
 
< 0.1%
5742
< 0.1%
5681
 
< 0.1%
5521
 
< 0.1%
5311
 
< 0.1%
5271
 
< 0.1%
5261
 
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5343
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2346.012483
Minimum0
Maximum7087
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:40.737625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11104
median1991
Q33326
95-th percentile5476
Maximum7087
Range7087
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1558.386209
Coefficient of variation (CV)0.664270212
Kurtosis-0.3815074
Mean2346.012483
Median Absolute Deviation (MAD)1036
Skewness0.7167263295
Sum258983356
Variance2428567.577
MonotonicityNot monotonic
2022-05-09T14:26:41.008013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150285
 
0.3%
618198
 
0.2%
990187
 
0.2%
1020175
 
0.2%
997174
 
0.2%
750169
 
0.2%
1530169
 
0.2%
900163
 
0.1%
1050162
 
0.1%
1410159
 
0.1%
Other values (5333)108552
98.3%
ValueCountFrequency (%)
022
 
< 0.1%
3053
< 0.1%
4229
 
< 0.1%
6047
 
< 0.1%
6766
0.1%
8572
0.1%
9071
0.1%
9568
0.1%
108124
0.1%
120110
0.1%
ValueCountFrequency (%)
70871
< 0.1%
70441
< 0.1%
70391
< 0.1%
70132
< 0.1%
70021
< 0.1%
69982
< 0.1%
69932
< 0.1%
69732
< 0.1%
69661
< 0.1%
69542
< 0.1%
Distinct5276
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.227632
Minimum0
Maximum7142
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size862.6 KiB
2022-05-09T14:26:41.288647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile417
Q11022
median1710
Q32553
95-th percentile4945
Maximum7142
Range7142
Interquartile range (IQR)1531

Descriptive statistics

Standard deviation1322.611941
Coefficient of variation (CV)0.6682465015
Kurtosis1.633616279
Mean1979.227632
Median Absolute Deviation (MAD)753
Skewness1.284940884
Sum218492876
Variance1749302.346
MonotonicityNot monotonic
2022-05-09T14:26:41.611006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618238
 
0.2%
607219
 
0.2%
541205
 
0.2%
765201
 
0.2%
990193
 
0.2%
700191
 
0.2%
1142189
 
0.2%
942186
 
0.2%
960183
 
0.2%
752180
 
0.2%
Other values (5266)108408
98.2%
ValueCountFrequency (%)
06
 
< 0.1%
3041
< 0.1%
4243
< 0.1%
6048
< 0.1%
6786
0.1%
8535
 
< 0.1%
9039
< 0.1%
9589
0.1%
10862
0.1%
12042
< 0.1%
ValueCountFrequency (%)
71421
< 0.1%
71171
< 0.1%
71111
< 0.1%
71102
< 0.1%
70951
< 0.1%
70911
< 0.1%
70822
< 0.1%
70811
< 0.1%
70751
< 0.1%
70651
< 0.1%

wilderness_area1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
61052 
1
49341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
061052
55.3%
149341
44.7%

Length

2022-05-09T14:26:41.895970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:42.190911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
061052
55.3%
149341
44.7%

Most occurring characters

ValueCountFrequency (%)
061052
55.3%
149341
44.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
061052
55.3%
149341
44.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
061052
55.3%
149341
44.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
061052
55.3%
149341
44.7%

wilderness_area2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
104711 
1
 
5682

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

Length

2022-05-09T14:26:42.409625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:42.655734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104711
94.9%
15682
 
5.1%

wilderness_area3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
62160 
1
48233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
062160
56.3%
148233
43.7%

Length

2022-05-09T14:26:42.868799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:43.097554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
062160
56.3%
148233
43.7%

Most occurring characters

ValueCountFrequency (%)
062160
56.3%
148233
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
062160
56.3%
148233
43.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
062160
56.3%
148233
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
062160
56.3%
148233
43.7%

wilderness_area4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
103256 
1
 
7137

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

Length

2022-05-09T14:26:43.286874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:43.510744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0103256
93.5%
17137
 
6.5%

soil_type_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109788 
1
 
605

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

Length

2022-05-09T14:26:43.699689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:43.938155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109788
99.5%
1605
 
0.5%

soil_type_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
108925 
1
 
1468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

Length

2022-05-09T14:26:44.129165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:44.349229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0108925
98.7%
11468
 
1.3%

soil_type_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109525 
1
 
868

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

Length

2022-05-09T14:26:44.555840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:44.947827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109525
99.2%
1868
 
0.8%

soil_type_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
108095 
1
 
2298

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

Length

2022-05-09T14:26:45.147239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:45.365101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0108095
97.9%
12298
 
2.1%

soil_type_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110098 
1
 
295

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

Length

2022-05-09T14:26:45.555683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:45.788270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110098
99.7%
1295
 
0.3%

soil_type_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109130 
1
 
1263

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

Length

2022-05-09T14:26:45.954041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:46.095900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109130
98.9%
11263
 
1.1%

soil_type_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110379 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

Length

2022-05-09T14:26:46.252656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:46.481416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110379
> 99.9%
114
 
< 0.1%

soil_type_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110358 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

Length

2022-05-09T14:26:46.684861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:46.911009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110358
> 99.9%
135
 
< 0.1%

soil_type_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110166 
1
 
227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

Length

2022-05-09T14:26:47.108353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:47.339337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110166
99.8%
1227
 
0.2%

soil_type_10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
104096 
1
 
6297

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

Length

2022-05-09T14:26:47.536324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:47.763320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104096
94.3%
16297
 
5.7%

soil_type_11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
107978 
1
 
2415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

Length

2022-05-09T14:26:47.942817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:48.182070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0107978
97.8%
12415
 
2.2%

soil_type_12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
104801 
1
 
5592

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

Length

2022-05-09T14:26:48.378354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:48.594903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104801
94.9%
15592
 
5.1%

soil_type_13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
107136 
1
 
3257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

Length

2022-05-09T14:26:48.782342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:49.020369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0107136
97.0%
13257
 
3.0%

soil_type_14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110277 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

Length

2022-05-09T14:26:49.221406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:49.436873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110277
99.9%
1116
 
0.1%

soil_type_15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110392 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

Length

2022-05-09T14:26:49.625705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:49.865731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110392
> 99.9%
11
 
< 0.1%

soil_type_16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109836 
1
 
557

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

Length

2022-05-09T14:26:50.053290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:50.318578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109836
99.5%
1557
 
0.5%

soil_type_17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109769 
1
 
624

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

Length

2022-05-09T14:26:50.574429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:50.807036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109769
99.4%
1624
 
0.6%

soil_type_18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110047 
1
 
346

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

Length

2022-05-09T14:26:50.968757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:51.283385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110047
99.7%
1346
 
0.3%

soil_type_19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109659 
1
 
734

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

Length

2022-05-09T14:26:51.498162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:51.730727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109659
99.3%
1734
 
0.7%

soil_type_20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
108604 
1
 
1789

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

Length

2022-05-09T14:26:51.928726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:52.172498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0108604
98.4%
11789
 
1.6%

soil_type_21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110243 
1
 
150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

Length

2022-05-09T14:26:52.353484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:52.590442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110243
99.9%
1150
 
0.1%

soil_type_22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
104086 
1
 
6307

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

Length

2022-05-09T14:26:52.788790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:53.025973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104086
94.3%
16307
 
5.7%

soil_type_23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
99476 
1
10917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

Length

2022-05-09T14:26:53.223487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:53.461491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

Most occurring characters

ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
099476
90.1%
110917
 
9.9%

soil_type_24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
106382 
1
 
4011

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

Length

2022-05-09T14:26:53.659181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:53.915173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0106382
96.4%
14011
 
3.6%

soil_type_25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110306 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

Length

2022-05-09T14:26:54.129171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:54.375170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110306
99.9%
187
 
0.1%

soil_type_26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
109881 
1
 
512

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

Length

2022-05-09T14:26:54.573170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:54.809227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109881
99.5%
1512
 
0.5%

soil_type_27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110161 
1
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

Length

2022-05-09T14:26:55.010226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:55.246169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110161
99.8%
1232
 
0.2%

soil_type_28
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110202 
1
 
191

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

Length

2022-05-09T14:26:55.445172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:55.671169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110202
99.8%
1191
 
0.2%

soil_type_29
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
88417 
1
21976 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

Length

2022-05-09T14:26:55.874290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:56.015784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

Most occurring characters

ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
088417
80.1%
121976
 
19.9%

soil_type_30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
104713 
1
 
5680

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

Length

2022-05-09T14:26:56.166040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:56.386518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0104713
94.9%
15680
 
5.1%

soil_type_31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
105479 
1
 
4914

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

Length

2022-05-09T14:26:56.587820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:56.819877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0105479
95.5%
14914
 
4.5%

soil_type_32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
100383 
1
 
10010

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

Length

2022-05-09T14:26:56.991934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:57.224655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0100383
90.9%
110010
 
9.1%

soil_type_33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
101685 
1
 
8708

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

Length

2022-05-09T14:26:57.421521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:57.833985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0101685
92.1%
18708
 
7.9%

soil_type_34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110087 
1
 
306

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

Length

2022-05-09T14:26:58.004679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:58.289791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110087
99.7%
1306
 
0.3%

soil_type_35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110021 
1
 
372

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

Length

2022-05-09T14:26:58.528173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:58.748470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110021
99.7%
1372
 
0.3%

soil_type_36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110374 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

Length

2022-05-09T14:26:58.935974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:59.185469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110374
> 99.9%
119
 
< 0.1%

soil_type_37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
110337 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

Length

2022-05-09T14:26:59.385530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:26:59.619525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0110337
99.9%
156
 
0.1%

soil_type_38
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
107451 
1
 
2942

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

Length

2022-05-09T14:26:59.794718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:27:00.035563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0107451
97.3%
12942
 
2.7%

soil_type_39
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
107886 
1
 
2507

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

Length

2022-05-09T14:27:00.238956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:27:00.470050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0107886
97.7%
12507
 
2.3%

soil_type_40
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
0
108698 
1
 
1695

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters110393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

Length

2022-05-09T14:27:00.672035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:27:00.904749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number110393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common110393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII110393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0108698
98.5%
11695
 
1.5%

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size862.6 KiB
Lodgepole_Pine
51682 
Spruce_Fir
38906 
Ponderosa_Pine
7375 
Krummholz
 
4508
Douglas_fir
 
3969
Other values (2)
 
3953

Length

Max length17
Median length14
Mean length12.10151006
Min length5

Characters and Unicode

Total characters1335922
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLodgepole_Pine
2nd rowSpruce_Fir
3rd rowSpruce_Fir
4th rowDouglas_fir
5th rowDouglas_fir

Common Values

ValueCountFrequency (%)
Lodgepole_Pine51682
46.8%
Spruce_Fir38906
35.2%
Ponderosa_Pine7375
 
6.7%
Krummholz4508
 
4.1%
Douglas_fir3969
 
3.6%
Aspen2614
 
2.4%
Cottonwood_Willow1339
 
1.2%

Length

2022-05-09T14:27:01.025642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T14:27:01.239759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
lodgepole_pine51682
46.8%
spruce_fir38906
35.2%
ponderosa_pine7375
 
6.7%
krummholz4508
 
4.1%
douglas_fir3969
 
3.6%
aspen2614
 
2.4%
cottonwood_willow1339
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e211316
15.8%
o133286
 
10.0%
_103271
 
7.7%
i103271
 
7.7%
r93664
 
7.0%
p93202
 
7.0%
n70385
 
5.3%
P66432
 
5.0%
l62837
 
4.7%
d60396
 
4.5%
Other values (19)337862
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1022956
76.6%
Uppercase Letter209695
 
15.7%
Connector Punctuation103271
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e211316
20.7%
o133286
13.0%
i103271
10.1%
r93664
9.2%
p93202
9.1%
n70385
 
6.9%
l62837
 
6.1%
d60396
 
5.9%
g55651
 
5.4%
u47383
 
4.6%
Other values (9)91565
9.0%
Uppercase Letter
ValueCountFrequency (%)
P66432
31.7%
L51682
24.6%
F38906
18.6%
S38906
18.6%
K4508
 
2.1%
D3969
 
1.9%
A2614
 
1.2%
C1339
 
0.6%
W1339
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_103271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1232651
92.3%
Common103271
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e211316
17.1%
o133286
10.8%
i103271
 
8.4%
r93664
 
7.6%
p93202
 
7.6%
n70385
 
5.7%
P66432
 
5.4%
l62837
 
5.1%
d60396
 
4.9%
g55651
 
4.5%
Other values (18)282211
22.9%
Common
ValueCountFrequency (%)
_103271
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1335922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e211316
15.8%
o133286
 
10.0%
_103271
 
7.7%
i103271
 
7.7%
r93664
 
7.0%
p93202
 
7.0%
n70385
 
5.3%
P66432
 
5.0%
l62837
 
4.7%
d60396
 
4.5%
Other values (19)337862
25.3%

Interactions

2022-05-09T14:26:30.634896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:14.494373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:17.220073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:19.921197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:22.547192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:25.148142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:27.804511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:30.968340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:14.999002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:17.613847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:20.285195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:22.918866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:25.540665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:28.198168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:31.236388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:15.485935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:18.111041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:20.876297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:23.289810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:25.968065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:28.561772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:31.594255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:15.859875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:18.464211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:21.111291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:23.652711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:26.246990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:28.943752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:31.971143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:16.114278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:18.817359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:21.493760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:24.028736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:26.650081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:29.314965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:32.376632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:16.471323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:19.175389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:21.830685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:24.414571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:27.040238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:29.698243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:32.738084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:16.849756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:19.549228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:22.180659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:24.784955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:27.447478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T14:26:30.255018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T14:27:01.660793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T14:27:04.035746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T14:27:06.326672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T14:27:08.928408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T14:27:09.985160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T14:26:34.247525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class
0275414651502179070010000000000000010000000000000000000000000000Lodgepole_Pine
1321921867-12869182500100000000000000000000000000000001000000000Spruce_Fir
2296533716427428832410000000000000000000000000000000100000000000Spruce_Fir
32368141515065100681200010000000001000000000000000000000000000000Douglas_fir
423661653390156116558200010000010000000000000000000000000000000000Douglas_fir
5310626754213716102710000000000000000000000000100000000000000000Spruce_Fir
6256890421223470129010000000000010000000000000000000000000000000Lodgepole_Pine
73001281544634365301710000000000000000000000000100000000000000000Lodgepole_Pine
833411331563813546624201000000000000000000000000000000000000000100Spruce_Fir
9309824201502250670000100000000000000000000000100000000000000000Spruce_Fir

Last rows

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class
110383226245400127160400010010000000000000000000000000000000000000Cottonwood_Willow
110384239310318277152417189400010000010000000000000000000000000000000000Cottonwood_Willow
110385237413120362177750159800010000010000000000000000000000000000000000Cottonwood_Willow
110386214910837124776464200010010000000000000000000000000000000000000Cottonwood_Willow
1103872063120130093319200011000000000000000000000000000000000000000Cottonwood_Willow
11038823142371739020124281500010100000000000000000000000000000000000000Cottonwood_Willow
110389228818440020143300010000000000100000000000000000000000000000Cottonwood_Willow
11039022321413285511188110400010010000000000000000000000000000000000000Cottonwood_Willow
1103912221163270073841700010001000000000000000000000000000000000000Cottonwood_Willow
110392222115033170101108247400010010000000000000000000000000000000000000Cottonwood_Willow

Duplicate rows

Most frequently occurring

elevationaspectslopehorizontal_distance_to_hydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHorizontal_Distance_To_Fire_Pointswilderness_area1wilderness_area2wilderness_area3wilderness_area4soil_type_1soil_type_2soil_type_3soil_type_4soil_type_5soil_type_6soil_type_7soil_type_8soil_type_9soil_type_10soil_type_11soil_type_12soil_type_13soil_type_14soil_type_15soil_type_16soil_type_17soil_type_18soil_type_19soil_type_20soil_type_21soil_type_22soil_type_23soil_type_24soil_type_25soil_type_26soil_type_27soil_type_28soil_type_29soil_type_30soil_type_31soil_type_32soil_type_33soil_type_34soil_type_35soil_type_36soil_type_37soil_type_38soil_type_39soil_type_40class# duplicates
2292185320271709678595300010000000001000000000000000000000000000000Douglas_fir5
1245254312924162-61412121800100001000000000000000000000000000000000000Ponderosa_Pine5
2643272417517212111211544600100001000000000000000000000000000000000000Ponderosa_Pine5
46972880499745802185104400100000000000100000000000000000000000000000Lodgepole_Pine5
75853005431712411355108400100000000000000000000000010000000000000000Spruce_Fir5
84493040200154221510239300100000000000000000000001000000000000000000Spruce_Fir5
14345340018314162452333339001000000000000000000000000000000000100000000Spruce_Fir5
143473401152124081152242222700100000000000000000000000000000000000000001Spruce_Fir5
146103650591910823952560123110000000000000000000000000000000000000000001Spruce_Fir5
7420575529170922129000011000000000000000000000000000000000000000Ponderosa_Pine4